{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modify the configure_optimizers() behavior of NeuralForecast models\n",
    "> Tutorial on how to achieve a full control of the `configure_optimizers()` behavior of NeuralForecast models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NeuralForecast models allow us to customize the default optimizer and learning rate scheduler behaviors via \n",
    "`optimizer`, `optimizer_kwargs`, `lr_scheduler`, `lr_scheduler_kwargs`. However this is not sufficient to support the use of [ReduceLROnPlateau](https://pytorch.org/docs/stable/generated/torch.optim.lr_scheduler.ReduceLROnPlateau.html), for instance, as it requires the specification of `monitor` parameter.\n",
    "\n",
    "This tutorial provides an example of how to support the use of `ReduceLROnPlateau`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/miniconda3/envs/neuralforecast/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "2025-02-25 15:57:21,708\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n",
      "2025-02-25 15:57:21,760\tINFO util.py:154 -- Missing packages: ['ipywidgets']. Run `pip install -U ipywidgets`, then restart the notebook server for rich notebook output.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "from neuralforecast import NeuralForecast\n",
    "from neuralforecast.models import NHITS\n",
    "from neuralforecast.utils import AirPassengersPanel\n",
    "\n",
    "from utilsforecast.plotting import plot_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "We use the AirPassengers dataset for the demonstration of conformal prediction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "AirPassengersPanel_train = AirPassengersPanel[AirPassengersPanel['ds'] < AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
    "AirPassengersPanel_test = AirPassengersPanel[AirPassengersPanel['ds'] >= AirPassengersPanel['ds'].values[-12]].reset_index(drop=True)\n",
    "AirPassengersPanel_test['y'] = np.nan\n",
    "AirPassengersPanel_test['y_[lag12]'] = np.nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model training\n",
    "\n",
    "We now train a NHITS model on the above dataset. We consider two different predictions:\n",
    "1. Training using the default `configure_optimizers()`.\n",
    "2. Training by overwriting the `configure_optimizers()` of the subclass of NHITS model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Seed set to 1\n",
      "Seed set to 1\n",
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name         | Type          | Params | Mode \n",
      "-------------------------------------------------------\n",
      "0 | loss         | MAE           | 0      | train\n",
      "1 | padder_train | ConstantPad1d | 0      | train\n",
      "2 | scaler       | TemporalNorm  | 0      | train\n",
      "3 | blocks       | ModuleList    | 2.4 M  | train\n",
      "-------------------------------------------------------\n",
      "2.4 M     Trainable params\n",
      "0         Non-trainable params\n",
      "2.4 M     Total params\n",
      "9.751     Total estimated model params size (MB)\n",
      "34        Modules in train mode\n",
      "0         Modules in eval mode\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.50it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.49it/s, v_num=85, train_loss_step=14.20, train_loss_epoch=14.20]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n",
      "\n",
      "  | Name         | Type          | Params | Mode \n",
      "-------------------------------------------------------\n",
      "0 | loss         | MAE           | 0      | train\n",
      "1 | padder_train | ConstantPad1d | 0      | train\n",
      "2 | scaler       | TemporalNorm  | 0      | train\n",
      "3 | blocks       | ModuleList    | 2.4 M  | train\n",
      "-------------------------------------------------------\n",
      "2.4 M     Trainable params\n",
      "0         Non-trainable params\n",
      "2.4 M     Total params\n",
      "9.751     Total estimated model params size (MB)\n",
      "34        Modules in train mode\n",
      "0         Modules in eval mode\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.78it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_steps=100` reached.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 99: 100%|██████████| 1/1 [00:00<00:00,  2.77it/s, v_num=86, train_loss_step=24.10, train_loss_epoch=24.10]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 33.39it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Predicting DataLoader 0: 100%|██████████| 1/1 [00:00<00:00, 246.29it/s]\n"
     ]
    }
   ],
   "source": [
    "horizon = 12\n",
    "input_size = 24\n",
    "\n",
    "class CustomNHITS(NHITS):\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adadelta(params=self.parameters(), rho=0.75)\n",
    "        scheduler=torch.optim.lr_scheduler.ReduceLROnPlateau(\n",
    "            optimizer=optimizer, mode='min',factor=0.5, patience=2,\n",
    "        )\n",
    "        scheduler_config = {\n",
    "            'scheduler': scheduler,\n",
    "            'interval': 'step',\n",
    "            'frequency': 1,\n",
    "            'monitor': 'train_loss',\n",
    "            'strict': True,\n",
    "            'name': None,\n",
    "        }\n",
    "        return {'optimizer': optimizer, 'lr_scheduler': scheduler_config}\n",
    "\n",
    "models = [\n",
    "    NHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-default-scheduler'),\n",
    "    CustomNHITS(h=horizon, input_size=input_size, max_steps=100, alias='NHITS-ReduceLROnPlateau-scheduler'),\n",
    "]\n",
    "nf = NeuralForecast(models=models, freq='ME')\n",
    "nf.fit(AirPassengersPanel_train)\n",
    "preds = nf.predict(futr_df=AirPassengersPanel_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x350 with 2 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_series(AirPassengersPanel_train, preds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can clearly notice the prediction outputs are different due to the change in `configure_optimizers()`."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
